Joint probability distribution

In summary: Fx(x,y) = \frac{6}{7} (\frac{2x^3}{3} + \frac{x^2}{2}) c) P(X<.5) = \frac{6}{7} \int_0^2(.5^2 + \frac{.5y}{2})dy P(X<.5) = \frac{6}{7} (\frac{.5}{4}(2)^2 + \frac{.5(2)^2}{2}) P(X<.5) = \frac{6}{7} (\frac{1}{2} + 1) P(X<.5) = \
  • #1
toothpaste666
516
20

Homework Statement


  1. The joint probability density function of X and Y is given by

    f(x,y)=(6/7)(x^2+ xy/2) , 0<x<1, 0<y<2.

    (a) Find the pdf of X.
    (b) Find the cdf of X.
    (c) FindP(X<.5).
    (d) Determine the conditional pdf of Y given X = x.

The Attempt at a Solution


a) the pdf is what is stated in the probem, that P(x,y) = (6/7)(x^2+ xy/2) , 0<x<1, 0<y<2. and P(x,y) = 0 elsewhere
b)
[itex] F(x,y) = \frac{6}{7} \int_0^Y \int_0^X(x^2 + \frac{xy}{2})dxdy [/itex]

[itex] = \frac{6}{7} \int_0^Y (\frac{X^3}{3} + \frac{X^2y}{4})dy [/itex]

[itex] = \frac{6}{7} (\frac{YX^3}{3} + \frac{X^2Y^2}{8})[/itex]

c)
[itex] \frac{6}{7} \int_0^2 \int_0^.5(x^2 + \frac{xy}{2})dxdy [/itex]

[itex] = \frac{6}{7} \int_0^2 (\frac{(.5)^3}{3} + \frac{(.5)^2y}{4})dy [/itex]

[itex] = \frac{6}{7} (\frac{(2)(.5)^3}{3} + \frac{(.5)^2(2)^2}{8})[/itex]

d) fy(y|x) = f(x,y)/fx(x)

[itex] fx(x) = \frac{6}{7}\int_0^2(x^2 + \frac{xy}{2})dy [/itex]

[itex] fx(x) = \frac{6}{7}(2x^2 + x) [/itex]

fy(y|x) = f(x,y)/fx(x) =

[itex] \frac{(6/7)(x^2+ xy/2)}{(6/7)(2x^2 + x)} [/itex]

[itex] = \frac{x(x+ y/2)}{x(2x + 1)} [/itex]

[itex] = \frac{(x+ y/2)}{(2x + 1)} [/itex]Is this correct?
 
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  • #2
toothpaste666 said:

Homework Statement


  1. The joint probability density function of X and Y is given by

    f(x,y)=(6/7)(x^2+ xy/2) , 0<x<1, 0<y<2.

    (a) Find the pdf of X.
    (b) Find the cdf of X.
    (c) FindP(X<.5).
    (d) Determine the conditional pdf of Y given X = x.

The Attempt at a Solution


a) the pdf is what is stated in the probem, that P(x,y) = (6/7)(x^2+ xy/2) , 0<x<1, 0<y<2. and P(x,y) = 0 elsewhere
b)
[itex] F(x,y) = \frac{6}{7} \int_0^Y \int_0^X(x^2 + \frac{xy}{2})dxdy [/itex]

[itex] = \frac{6}{7} \int_0^Y (\frac{X^3}{3} + \frac{X^2y}{4})dy [/itex]

[itex] = \frac{6}{7} (\frac{YX^3}{3} + \frac{X^2Y^2}{8})[/itex]

c)
[itex] \frac{6}{7} \int_0^2 \int_0^.5(x^2 + \frac{xy}{2})dxdy [/itex]

[itex] = \frac{6}{7} \int_0^2 (\frac{(.5)^3}{3} + \frac{(.5)^2y}{4})dy [/itex]

[itex] = \frac{6}{7} (\frac{(2)(.5)^3}{3} + \frac{(.5)^2(2)^2}{8})[/itex]

d) fy(y|x) = f(x,y)/fx(x)

[itex] fx(x) = \frac{6}{7}\int_0^2(x^2 + \frac{xy}{2})dy [/itex]

[itex] fx(x) = \frac{6}{7}(2x^2 + x) [/itex]

fy(y|x) = f(x,y)/fx(x) =

[itex] \frac{(6/7)(x^2+ xy/2)}{(6/7)(2x^2 + x)} [/itex]

[itex] = \frac{x(x+ y/2)}{x(2x + 1)} [/itex]

[itex] = \frac{(x+ y/2)}{(2x + 1)} [/itex]Is this correct?

No: (a) and (b) are wrong. You are given ##f_{XY}(x,y)##, and have been asked to find ##f_X(x)## and ##F_X(x)##, the marginal pdf and cdf of ##X## alone. Anyway, what you wrote in (b) makes no sense: you have numbers ##x## and ##y## on the left in ##F(x,y)## but have random variables ##X## and ##Y## on the right.

I have not checked the details of (c), but the basic calculations look OK. Your (d) looks OK as well.
 
  • #3
a)
[itex] fx(x,y) = \frac{6}{7} \int_0^2 (x^2 + \frac{xy}{2})dy [/itex]

[itex] fx(x,y) = \frac{6}{7} ((2)x^2 + \frac{x(2)^2}{4}) [/itex]

[itex] fx(x,y) = \frac{6}{7} (2x^2 + x) [/itex]

b)
[itex] Fx(x,y) = \frac{6}{7} \int_0^x(2u^2 + u)du [/itex]

[itex] Fx(x,y) = \frac{6}{7} (\frac{2x^3}{3} + \frac{x^2}{2}) [/itex]
 

1. What is a joint probability distribution?

A joint probability distribution is a statistical concept that describes the probability of two or more random variables occurring simultaneously. It is used to model the relationship between multiple variables and their outcomes.

2. How is a joint probability distribution different from a marginal probability distribution?

A joint probability distribution takes into account the probabilities of multiple variables occurring together, while a marginal probability distribution calculates the probabilities of individual variables occurring independently. In other words, a joint probability distribution considers the joint occurrences of two or more variables, while a marginal probability distribution only considers the probability of one variable at a time.

3. What is the importance of joint probability distributions in statistics?

Joint probability distributions are important in statistics because they provide a way to model the relationship between multiple variables and their outcomes. This allows us to better understand the dependencies and correlations between variables, which can be useful in making predictions and drawing conclusions from data.

4. How is a joint probability distribution calculated?

A joint probability distribution is calculated by multiplying the individual probabilities of each variable occurring together. For example, if the probability of event A is 0.2 and the probability of event B is 0.3, the joint probability of both events occurring is 0.2 x 0.3 = 0.06.

5. Can a joint probability distribution be used to calculate conditional probabilities?

Yes, a joint probability distribution can be used to calculate conditional probabilities. By dividing the joint probability of two events by the marginal probability of one of the events, we can calculate the probability of one event occurring given that the other event has already occurred.

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